


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__			:      Dict								=get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
@require_tokenizers
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						XLMRobertaTokenizer
							_UpperCAmelCase      =						XLMRobertaTokenizerFast
							_UpperCAmelCase      =						True
							_UpperCAmelCase      =						True






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().setUp()

										# We have a SentencePiece fixture for testing
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					XLMRobertaTokenizer(lowerCAmelCase__			,						keep_accents=lowerCAmelCase__       )
										tokenizer.save_pretrained(self.tmpdirname       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					'<pad>'
										SCREAMING_SNAKE_CASE_		:     List[str]					=					1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__       )			,						lowerCAmelCase__       )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__       )			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					list(self.get_tokenizer().get_vocab().keys()       )

										self.assertEqual(vocab_keys[0]			,						'<s>'       )
										self.assertEqual(vocab_keys[1]			,						'<pad>'       )
										self.assertEqual(vocab_keys[-1]			,						'<mask>'       )
										self.assertEqual(len(lowerCAmelCase__       )			,						1_0_0_2       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										self.assertEqual(self.get_tokenizer().vocab_size			,						1_0_0_2       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					XLMRobertaTokenizer(lowerCAmelCase__			,						keep_accents=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					tokenizer.tokenize('This is a test'       )
										self.assertListEqual(lowerCAmelCase__			,						['▁This', '▁is', '▁a', '▁t', 'est']       )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )			,						[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,						)

										SCREAMING_SNAKE_CASE_		:     str					=					tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
										self.assertListEqual(
										    lowerCAmelCase__			,						[
										        SPIECE_UNDERLINE + 'I',
										        SPIECE_UNDERLINE + 'was',
										        SPIECE_UNDERLINE + 'b',
										        'or',
										        'n',
										        SPIECE_UNDERLINE + 'in',
										        SPIECE_UNDERLINE + '',
										        '9',
										        '2',
										        '0',
										        '0',
										        '0',
										        ',',
										        SPIECE_UNDERLINE + 'and',
										        SPIECE_UNDERLINE + 'this',
										        SPIECE_UNDERLINE + 'is',
										        SPIECE_UNDERLINE + 'f',
										        'al',
										        's',
										        'é',
										        '.',
										    ]			,						)
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )
										self.assertListEqual(
										    lowerCAmelCase__			,						[
										        value + tokenizer.fairseq_offset
										        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]			,						)

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.convert_ids_to_tokens(lowerCAmelCase__       )
										self.assertListEqual(
										    lowerCAmelCase__			,						[
										        SPIECE_UNDERLINE + 'I',
										        SPIECE_UNDERLINE + 'was',
										        SPIECE_UNDERLINE + 'b',
										        'or',
										        'n',
										        SPIECE_UNDERLINE + 'in',
										        SPIECE_UNDERLINE + '',
										        '<unk>',
										        '2',
										        '0',
										        '0',
										        '0',
										        ',',
										        SPIECE_UNDERLINE + 'and',
										        SPIECE_UNDERLINE + 'this',
										        SPIECE_UNDERLINE + 'is',
										        SPIECE_UNDERLINE + 'f',
										        'al',
										        's',
										        '<unk>',
										        '.',
										    ]			,						)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										if not self.test_slow_tokenizer:
													# as we don't have a slow version, we can't compare the outputs between slow and fast versions
													return

										SCREAMING_SNAKE_CASE_		:     str					=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Tuple					=					self.tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__       )

																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE_		:     str					=					tokenizer_r.save_pretrained(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer_p.save_pretrained(lowerCAmelCase__       )

																# Checks it save with the same files + the tokenizer.json file for the fast one
																self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files       )       )
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f       )
																self.assertSequenceEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tokenizer_r.from_pretrained(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_p.from_pretrained(lowerCAmelCase__       )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )
																			# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																			# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																shutil.rmtree(lowerCAmelCase__       )

																# Save tokenizer rust, legacy_format=True
																SCREAMING_SNAKE_CASE_		:     int					=					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_r.save_pretrained(lowerCAmelCase__			,						legacy_format=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_p.save_pretrained(lowerCAmelCase__       )

																# Checks it save with the same files
																self.assertSequenceEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_r.from_pretrained(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     str					=					tokenizer_p.from_pretrained(lowerCAmelCase__       )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )

																shutil.rmtree(lowerCAmelCase__       )

																# Save tokenizer rust, legacy_format=False
																SCREAMING_SNAKE_CASE_		:     Any					=					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer_r.save_pretrained(lowerCAmelCase__			,						legacy_format=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     int					=					tokenizer_p.save_pretrained(lowerCAmelCase__       )

																# Checks it saved the tokenizer.json file
																self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files       )       )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_r.from_pretrained(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer_p.from_pretrained(lowerCAmelCase__       )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )

																shutil.rmtree(lowerCAmelCase__       )






							@cached_property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										with tempfile.NamedTemporaryFile() as f:
													shutil.copyfile(lowerCAmelCase__			,						f.name       )
													SCREAMING_SNAKE_CASE_		:     List[str]					=					XLMRobertaTokenizer(f.name			,						keep_accents=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Dict					=					pickle.dumps(lowerCAmelCase__       )
										pickle.loads(lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										if not self.test_rust_tokenizer:
													return

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_tokenizer()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_rust_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Any					=					'I was born in 92000, and this is falsé.'

										SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer.tokenize(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					rust_tokenizer.tokenize(lowerCAmelCase__       )
										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					rust_tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_rust_tokenizer()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.encode(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					rust_tokenizer.encode(lowerCAmelCase__       )
										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'Hello World!'
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

										self.assertListEqual(lowerCAmelCase__			,						self.big_tokenizer.encode(lowerCAmelCase__       )       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(
										    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
										    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
										)
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[
										    0,
										    3_2_9_3,
										    8_3,
										    1_0,
										    4_5_5_2,
										    4_9_8_9,
										    7_9_8_6,
										    6_7_8,
										    1_0,
										    5_9_1_5,
										    1_1_1,
										    1_7_9_4_5_9,
										    1_2_4_8_5_0,
										    4,
										    6_0_4_4,
										    2_3_7,
										    1_2,
										    6,
										    5,
										    6,
										    4,
										    6_7_8_0,
										    7_0_5,
										    1_5,
										    1_3_8_8,
										    4_4,
										    3_7_8,
										    1_0_1_1_4,
										    7_1_1,
										    1_5_2,
										    2_0,
										    6,
										    5,
										    2_2_3_7_6,
										    6_4_2,
										    1_2_2_1,
										    1_5_1_9_0,
										    3_4_1_5_3,
										    4_5_0,
										    5_6_0_8,
										    9_5_9,
										    1_1_1_9,
										    5_7_7_0_2,
										    1_3_6,
										    1_8_6,
										    4_7,
										    1_0_9_8,
										    2_9_3_6_7,
										    4_7,
										    # 4426, # What fairseq tokenizes from "<unk>": "_<"
										    # 3678, # What fairseq tokenizes from "<unk>": "unk"
										    # 2740, # What fairseq tokenizes from "<unk>": ">"
										    3,  # What we tokenize from "<unk>": "<unk>"
										    6,  # Residue from the tokenization: an extra sentencepiece underline
										    4,
										    6_0_4_4,
										    2_3_7,
										    6_2_8_4,
										    5_0_9_0_1,
										    5_2_8,
										    3_1,
										    9_0,
										    3_4,
										    9_2_7,
										    2,
										]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

										self.assertListEqual(lowerCAmelCase__			,						self.big_tokenizer.encode(lowerCAmelCase__       )       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					{'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=lowerCAmelCase__			,						model_name='xlm-roberta-base'			,						revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'			,						)









"""simple docstring"""



import math
from datetime import datetime, timedelta
def      UpperCAmelCase     (       _lowercase :   int	)						-> datetime:


	"""simple docstring"""




	lowerCAmelCase_      							=			year % 1_9
	lowerCAmelCase_      							=			year % 4
	lowerCAmelCase_      							=			year % 7
	lowerCAmelCase_      							=			math.floor(year / 1_0_0	)
	lowerCAmelCase_      							=			math.floor((1_3 + 8 * leap_day_inhibits) / 2_5	)
	lowerCAmelCase_      							=			leap_day_inhibits / 4
	lowerCAmelCase_      							=			(
	    1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
	) % 3_0
	lowerCAmelCase_      							=			(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

	# days to be added to March 21
	lowerCAmelCase_      							=			(1_9 * metonic_cycle + secular_moon_shift) % 3_0

	# PHM -> Paschal Full Moon
	lowerCAmelCase_      							=			(
	    2 * julian_leap_year
	    + 4 * non_leap_year
	    + 6 * days_to_add
	    + century_starting_point
	) % 7

	if days_to_add == 2_9 and days_from_phm_to_sunday == 6:
		return datetime(_lowercase			,   4			,   1_9	)
	elif days_to_add == 2_8 and days_from_phm_to_sunday == 6:
		return datetime(_lowercase			,   4			,   1_8	)
	else:
		return datetime(_lowercase			,   3			,   2_2	) + timedelta(
		    days=int(days_to_add + days_from_phm_to_sunday	)	)


if __name__ == "__main__":
		for year in (19_94, 20_00, 20_10, 20_21, 20_23):
				lowercase_							=							'will be' if year > datetime.now().year else 'was'
				print(f"""Easter in {year} {tense} {gauss_easter(year)}""")